https://github.com/goldsharon/sentimaster
Sentimaster is an AI-powered web tool that analyzes restaurant reviews. It uses a fine-tuned GPT-2 model to classify sentiment, giving businesses real-time insights for better service and decision-making.
https://github.com/goldsharon/sentimaster
ai aws customerfeedback deeplearning flask gpt2 machinelearning modeldeployment naturallanguageprocessing nlp pytorch sentimentanalysis webapplication
Last synced: 6 months ago
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Sentimaster is an AI-powered web tool that analyzes restaurant reviews. It uses a fine-tuned GPT-2 model to classify sentiment, giving businesses real-time insights for better service and decision-making.
- Host: GitHub
- URL: https://github.com/goldsharon/sentimaster
- Owner: GoldSharon
- License: mit
- Created: 2024-12-05T15:17:50.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-05T17:54:30.000Z (10 months ago)
- Last Synced: 2025-03-29T07:44:57.843Z (6 months ago)
- Topics: ai, aws, customerfeedback, deeplearning, flask, gpt2, machinelearning, modeldeployment, naturallanguageprocessing, nlp, pytorch, sentimentanalysis, webapplication
- Language: Jupyter Notebook
- Homepage:
- Size: 216 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# **Sentimaster: Sentiment Analysis Tool**
Sentimaster is a web-based sentiment analysis tool developed to analyze restaurant reviews using AI-powered sentiment classification. Built using a fine-tuned GPT-2 model, Sentimaster empowers businesses with real-time actionable insights from customer feedback to improve customer service and decision-making.
---
## **Tech Stack**
- **GPT-2** (124M parameters) for sentiment analysis model
- **Flask** for backend web framework
- **AWS EC2** for deployment
- **HTML**, **CSS**, **JavaScript** for frontend development
- **PyTorch** for model training and inference
- **Tiktoken** for tokenization
---
## **Features**
- **Sentiment Analysis**: Classifies restaurant reviews as either "Positive" or "Negative".
- **Real-Time Feedback**: Users can submit reviews through the web interface and receive real-time sentiment analysis.
- **Model**: Fine-tuned GPT-2 (124M parameters) on restaurant-specific data for improved accuracy.
- **Deployment**: Deployed using Flask, accessible via a web browser.
---
## **Installation**
1. Clone this repository to your local machine:
```bash
git clone https://github.com/GoldSharon/Sentimaster.git
```
2. Navigate to the project directory:
```bash
cd Sentimaster
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Ensure that you have the pretrained model weights (`model_and_optimizer.pth`) placed in the project directory.
- If the model weights are not available, follow the training guidelines provided in the documentation or scripts to train the model.
---
## **Usage**
1. Run the Flask app:
```bash
python app.py
```
2. The application will be available at:
[http://127.0.0.1:5000/](http://127.0.0.1:5000/)
3. Open your browser and go to the URL. Enter a restaurant review in the text field, and the model will classify the sentiment as "Positive" or "Negative".
---
## **Model Architecture**
- **GPT-2 (124M parameters)** is used for sentiment analysis, fine-tuned on restaurant-related reviews to improve domain-specific accuracy.
- **Architecture Details**:
- 12 layers, 768 embedding dimensions, and 12 attention heads.
- Trained and optimized using the Adam optimizer with a learning rate of 0.0004.
---
## **API Endpoints**
- `/`: Home route, renders the input form for the review.
- `/submit`: POST method, accepts the review and returns sentiment classification.
- `/result`: Displays the result of the sentiment analysis.
---
## **Example Workflow**
1. The user submits a restaurant review via the form.
2. The model classifies the sentiment of the review (Positive/Negative).
3. The result is displayed on a new page, showing whether the review is positive or negative.
---
## **Impact**
By using Sentimaster, businesses can:
- Gain insights from customer feedback.
- Improve customer service and satisfaction.
- Make data-driven decisions for business growth and improvement.
---
## **Contributions**
Feel free to fork the repository, create issues, and submit pull requests. Contributions are always welcome!
---
## **License**
This project is licensed under the **MIT License**.
---